Travel-Time Disparities in Access to Proton Beam Therapy for Cancer Treatment

Key Points Question Do certain US populations have inequitable drive-time access to proton beam therapy for cancer treatment? Findings In this population-based cross-sectional study of 327 536 032 residents of the contiguous US, individuals aged 65 years and older, living below the federal poverty line, and residing in suburban and rural areas were at greatest risk of having long commutes (≥4 hours) to proton beam therapy for cancer treatment. Meaning The current geographic distribution of proton beam therapy facilities in the US is associated with inequitable access to proton beam therapy as a cancer treatment option and may hinder enrollment in ongoing clinical trials.


Introduction
Compared with photon-based radiotherapy, proton beam therapy (PBT) may have an improved toxicity profile due to decreased dose to organs at risk without compromising target coverage. 1High upfront levels of investment in equipment and facilities have led to uneven distribution of proton therapy facilities in the United States. 2 Clinical indications and insurance reimbursement for PBT are increasing but access remains difficult for many individuals given the limited number of available treatment facilities. 3Furthermore, patients and families are often required to travel daily for several weeks during treatment, thus compounding the importance of geographic barriers to access.In order to encourage provision of equitable care among diverse populations, it is critical to identify at-risk groups that face travel-based barriers to care.Such knowledge could further inform policy to improve clinical outcomes and participation in clinical trials aimed at evaluating the efficacy of PBT across a myriad of clinical scenarios.Geographic accessibility, in particular drive time and driving distance, is associated with patient utilization of both proton and photon radiotherapy. 4,5A prior investigation by Mallie et al 6 in 2021 described variation in PBT drive-time accessibility among 36 facilities, with a focus on pediatric and adult populations and disparities at the state and regional level.However, that analysis was largely descriptive in nature without an attempt to address potential confounding between causative variables to explain disparities observed.To our knowledge, a study investigating the geographic accessibility of proton therapy facilities that considers social determinants of health (SDOH) has yet to be performed.
In this study, we examined the drive-time accessibility of all full-service proton facilities operational in the US as of September 2023.Furthermore, we sought to uncover multivariable associations among these factors that would better describe accessibility issues than an unadjusted approach.

Methods
This cross-sectional study was deemed exempt from review and informed consent was waived by the University of Kentucky institutional review board because we used only publicly available deidentified data, in accordance with 45 CFR §46.We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Data Sources
We compiled a list of 42 PBT facilities listed as "currently operating" by the National Association for Proton Therapy on September 1, 2023.We excluded 2 facilities that primarily treat children (St Jude Children's Research Hospital and Cincinnati Children's Proton Therapy Center).Geocoding in ArcGIS Business Analyst Pro (ESRI ArcGIS Pro 3.1) provided coordinates for all included PBT facility addresses (eTable 1 in Supplement 1).We collected census tract population estimates from the US Census Bureau's 5-Year (2017-2021) American Community Survey estimates using the tidycensus package version 1.4.1 in the R software environment version 4.2.3. 7,8The American Community Survey is a survey of population characteristics collected from a nationwide sample of approximately 3.5 million households annually.
Along with total population, we included counts and percentage of the total population by census tract for the following population subgroups: Hispanic (all races), non-Hispanic Asian, non-Hispanic  Since Rural-Urban Commuting Area codes were not available for tracts defined by the 2020

Statistical Analysis
We calculated traffic-aware driving times from the land-based geographic centroid of every census tract in the contiguous US to the nearest PBT facility using the ArcGIS US 2023 Business Analyst dataset with network routes based on contemporaneous roads and average traffic patterns.Driving times were unavailable for 49 tracts due to their centroid being located in a geographically remote area without road access.We imputed driving times for these tracts using a k-nearest neighbor mean of the driving times for the 5 nearest census tracts.We measured driving times in minutes on a continuous scale and calculated 2 categorical driving-time variables: driving times of 0 to less than 1 hour, 1 hour to less than 2 hours, 2 hours to less than 3 hours, 3 hours to less than 4 hours, and 4 or more hours; and a dichotomous indicator for very long commutes and/or poor vehicle access (driving time of 4 or more hours).
We assigned the driving time from the centroid of a census tract to all population totals for that tract and calculated (weighted) median driving times and corresponding IQRs.We stratified census tracts into quintiles based on increasing population percentage for demographic and SDOH factors and compared driving-time distributions.Because analyses of driving times apply to the entire population of the contiguous US, it was not appropriate to perform tests of statistical significance.
We built binary logistic regression models using the dichotomous driving-time variable to identify SDOH factors associated with poor vehicle access to PBT facilities. 11First, we performed univariable analyses, and then constructed a full model using all statistically significant univariable factors.We applied backward stepwise selection with Bayesian Information Criteria to arrive at a final multivariable model.The final set of covariates considered for logistic regression analysis included percentages of the population that were (1) aged 65 years or older, (2) uninsured, (3) living below the federal poverty line, and (4) living with a disability; along with percentages of households with (5)   limited vehicle availability and ( 6) no broadband access (all measured in increments of 10 percentage points).We added urbanicity to the final multivariable model to account for its association with SDOH factors.All statistical tests performed were 2-sided with a P < .05significance level; odds ratios (ORs) and 95% CIs are reported.We used the ResourceSelection version 0. September to November 2023. 12,13 visualize spatial patterns in SDOH variables and driving times to the nearest PBT facility, we constructed bivariate choropleth maps using ArcGIS Pro.First, we identified spatial clusters of high and low values for the statistically significant SDOH variables (percentage of population age Ն65 years, living below federal poverty line) by calculating the Getis-Ord Gi* statistic for each census tract.
We used fixed distance bands of 25 miles and applied the false discovery rate correction.We then mapped the Getis-Ord Gi* statistics for each SDOH variable against driving time to the nearest PBT

JAMA Network Open | Oncology
Travel-Time Disparities in Access to Proton Beam Therapy for Cancer Treatment facility.We used a 3 × 3 legend, with cluster z-scores split at −2.576 and +2.576 and driving times split at 2 and 4 hours.We included currently operating PBT facilities and NCI-designated Cancer Centers as additional features in these maps.

Results
We

Visualizing Factors Associated With Poor Vehicle Access
Bivariate maps demonstrate the spatial association between driving times to the nearest PBT facility and the 2 SDOH variables with significantly increased odds of poor vehicle access in the final multivariable model (Figure 2).In particular, the bright red areas (high-high) indicate census tracts that were at least 4 hours from the nearest PBT facility and in a statistically significant hotspot for percentage of the population aged 65 years or older (or, alternatively, population living below the federal poverty line).

Driving Times to NCI-Designated Cancer Centers
Of the 63 NCI-designated cancer centers analyzed, 23 were run or affiliated with existing PBT facilities, with a median (IQR) driving time between locations of 4.6 (1.8-13.7)minutes.The remaining 40 centers had a median (IQR) driving time to the nearest PBT facility of 132.5 (60.6-199.1 minutes).Six cancer centers were located at least 4 hours from the nearest PBT facility (Figure 2; eTables 2-3 in Supplement 1).

Discussion
In this investigation, we identified extreme heterogeneity in drive-time accessibility to PBT facilities across the US, with only 36.6% of the population living within 1 hour of a currently operating facility, and 16.4% at a driving distance of 4 or more hours.Furthermore, we identified significant  b Population and population subgroup driving times calculated by assigning the applicable number of individuals as weights to their corresponding census tract driving times and computing the weighted medians and interquartile ranges.
c Poor access to PBT facilities is defined as a 1-way commute of greater than or equal to 4 hours to the nearest facility.Maillie et al 6 published a brief report in 2021 describing PBT accessibility for the 36 facilities operational in the US at that time, with a specific focus on differences between adult and pediatric populations. 6Median drive times for pediatric (1.61 hours) and adult (1.64 hours) populations were reported.They additionally reported findings by state and US region, showing large variation across these levels.Our investigation confirms and builds upon this previous work, using an updated list of PBT facilities and a more in-depth analysis of population demographics and SDOH variables.
The receipt of radiotherapy (either proton-or photon-based) presents unique travel-related challenges for patients compared with other oncologic treatment modalities.The effects of increased travel time on oncologic surgery and systemic therapies are mostly relegated to the acute phases of diagnosis and/or treatment, when the need for specialty care tied to a specific location is highest.In contrast, a course of radiotherapy typically includes several weeks of daily outpatient treatment at a specialized facility.7][18] Our findings that individuals aged 65 years and older and those living below the federal poverty line are more likely to experience poor access to PBT specifically further complicates matters.Research [21][22][23][24] Understanding these barriers is also important with respect to future research on PBT.At the time of this writing, there are several indications for PBT supported by the National Comprehensive Cancer Network (NCCN) guidelines, including select pediatric tumors, thymic tumors, uveal melanomas, esophageal cancers, some Hodgkin and other lymphomas, seminomas, and chordomas. 25There is also growing interest in using PBT to treat prostate cancer, head and neck cancers, and breast cancer.Critics of PBT point to a lack of randomized data supporting its use among these diagnoses.In order to rigorously study the potential benefits of PBT, clinical trials are necessary and ongoing.The presence of travel-related barriers may prevent successful trial completion or lead to results that inadequately represent the general population due to selection bias toward those able to overcome travel-related hardships.That said, our finding of lower drive times to PBT facilities for Hispanic, non-Hispanic Asian, and non-Hispanic Black individuals in the US presents an opportunity to enhance clinical trial enrollment for underrepresented racial and ethnic minority groups.
It is critical to recognize that drive time to a PBT facility is one of many factors associated with realized health care accessibility.Differences in health care literacy that correlate with education may affect a patients' propensity to seek treatment regardless of geographic accessibility. 268][29] While Black individuals have shorter driving times to PBT facilities, a retrospective analysis of patients with prostate cancer found that the ratio of Black individuals compared with other races receiving PBT was lower than the ratio of Black individuals receiving intensity modulated radiation therapy. 30The higher cost of proton therapy and the higher rates of living below the federal poverty line and uninsured persons among Black individuals likely explain this difference. 31

Limitations
This study has limitations.Although this study draws strength from a comprehensive data source to explore granular associations at a national scale, it nonetheless is limited by factors inherently related to the study design.This analysis assumed that patients with cancer will travel to the PBT facility with the shortest commute, which may not happen in practice.Additionally, we only analyzed personal time and money.Due to the absence of certain racial and ethnic demographics in many census tracts, we could only analyze US residents who were Hispanic, non-Hispanic Asian, non-Hispanic Black, and non-Hispanic White.This analysis may also underestimate the geographic access for pediatric patients because we excluded 2 clinics that serve them exclusively.Personal travel times will vary from those assessed using the census tract geographic centroid, with greater discrepancies possible in larger, more sparsely populated tracts.

Conclusion
In this nationwide cross-sectional study of driving time accessibility to proton therapy facilities, we found that geographic isolation via road travel was associated with certain patient populations, such as older individuals (aged Ն65 years), those living below the federal poverty line, and suburban or rural residents.These results have important implications for disparities in health care delivery and may also affect the successful accrual of clinical trials evaluating the efficacy of proton therapy.

3 - 6
and car version 3.1-2 packages in R version 4.2.3 (R Project for Statistical Computing) to calculate goodness of fit and variance inflation factors (VIFs) for all models.All statistical analyses were performed from

Figure 1 .a
Figure 1.Driving Time to Nearest Proton Beam Therapy (PBT) Facility From Census Tract Land-Based Geographic Centroid a demographic and SDOH factors associated with PBT access, highlighting current disparities to receiving PBT, either for routine clinical indications or as part of a clinical trial.Patients who are older (aged Ն65 years), living below the poverty line, and living in suburban or rural areas, were at highest risk of geographic isolation from PBT facilities.Non-Hispanic White individuals tended to have longer drive-times, likely related to higher population percentages in suburban and rural regions.Drivetime accessibility presents a substantial barrier to the delivery of PBT at scale for the US population today.While temporary overnight accommodations near a proton therapy facility are an option, most people with cancer prefer to receive their treatments in a location that allows for continued residence in the home environment.Furthermore, overnight accommodations are often not covered by vehicular drive time and did not account for travel via flying, public transportation, or nonvehicular modes of transportation.This likely resulted in underestimating travel times for those without personal vehicles in urban areas, as those individuals might rely on public transportation, taxis, ride sharing, or other modes that require greater resources than a personal vehicle trip in terms of both JAMA Network Open | Oncology Travel-Time Disparities in Access to Proton Beam Therapy for Cancer Treatment JAMA Network Open.2024;7(5):e2410670.doi:10.1001/jamanetworkopen.2024.10670(Reprinted) May 17, 2024 8/11 Downloaded from jamanetwork.comby guest on 05/19/2024 Travel-Time Disparities in Access to Proton Beam Therapy for Cancer Treatment Black, non-Hispanic White, aged 65 years or older, living below the federal poverty line, unemployed, uninsured, and living with a disability.We also collected count and percentage of households by census tract with limited vehicle availability (1 or fewer available vehicles) and no broadband internet access.Race and ethnicity variables were classified in accord with methods used in the 2017 to 2021 American Community Survey.

Table 2
displays median driving times and percentage of population (or households) with poor vehicle access (Ն4 hours away) to PBT facilities for various population subgroups.Median (IQR) driving times for non-Hispanic White (109.8[48.0-197.6]minutes), those aged 65 years or older Travel-Time Disparities in Access to Proton Beam Therapy for Cancer Treatment (103.0 [43.6-196.7]minutes),thosewitha disability (107.7 [44.8-201.1]minutes),those living below the federal poverty line (103.6 [40.6-201.1]minutes),households with no broadband access (114.4 [47.0-202.1]minutes),and those living in suburban (164.8 [109.3-229.9]minutes)and rural (190.3 [143.9-276.1]minutes)areas were numerically higher than the population median.A disproportionate amount of the populations of non-Hispanic Asian (12.2 million [20.0%]), aged 65 years and older (8.7 million [16.6%]), persons living with a disability (6.9 million [17.0%]), personsAnalyzing census tract driving-time distributions by quintiles for racial and ethnic subgroups, we found that median driving times increased with increasing quintiles of non-Hispanic White population and decreased with increasing quintiles of Hispanic, non-Hispanic Asian, and non-Hispanic Black populations (eFigure in Supplement 1).Likewise, median driving times increased with increasing quintiles of population age 65 years or older, living with a disability and with no household broadband access (eFigure in Supplement 1).Median drives times generally increased for increasing quintiles of living below the federal poverty line, with the exception of a slight decrease in the last quintile.Populations uninsured and with limited vehicle availability were found to have nonlinear associations between median driving times and increasing quintiles, with the last quintile of households with limited vehicle availability experiencing the shortest median driving time.

Table 3
summarizes univariable and multivariable logistic regressions to identify census tract-level covariates associated with poor vehicle access to PBT facilities.The percentage of population uninsured was not significant under univariable analysis and was therefore excluded from the full multivariable model.Percentage with no broadband access was not significant in the full model.Backward selection removed population with no broadband access and population living with a disability.The remaining variables were retained and significant in the final multivariable model.Under this model, the odds of poor vehicle access to PBT facilities increased for each 10% increase in population aged 65 years or older (OR, 1.09 [95% CI, 1.06-1.11])and living below the federal poverty line (OR, 1.22 [95% CI, 1.20-1.25]),and decreased for each 10% increase in population with limited vehicle availability (OR, 0.83 [95% CI, 0.82-0.84]).Compared with urban census tracts, suburban census tracts had a 40.6% increase (OR, 1.41 [95% CI, 1.33-1.48])and rural census tracts had a 144.5% increase (OR, 2.45 [95% CI, 2.27-2.64]) in odds of poor access to PBT facilities.The final multivariable model demonstrated goodness of fit via the Hosmer and Lemeshow test (P = .23)and all independent variables had VIF less than 2.

Table 1 .
Population Access to Proton Beam Therapy Facilities by Driving Time Categories in the United States

Table 2 .
Comparison of Access to Proton Beam Therapy Facilities by Population Subgroups in the United States, 2017-2021 a a Data are from the American Community Survey 5-Year Estimates, 2017 to 2021.

Table 3 .
Logistic Regression Analyses to Identify Covariates Associated With a 4-Hour Drive or Longer to Nearest Proton Beam Therapy Facility a ORs are given per quintile increase in variable of interest.bMultivariable model adjusted for age, vehicle availability, poverty status, and urbanicity.